The original approach to MAIC uses a robust sandwich-type variance estimator [1]} to compute the standard error of \(\hat{\Delta }_{10}^{(2)}\) . This relies on large-sample properties and has understated variability with small ESSs in a previous simulation study investigating MAIC [2]} and in other settings [3]}, [4]}, [5]}, [6]}. In addition, most implementations of the sandwich estimator, e.g. when fitting the weighted regression [7]}, ignore the estimation of the trial assignment model, assuming the weights to be fixed quantities. While analytic expressions that incorporate the estimation of the weights could be derived, a practical alternative is to resample via the ordinary non-parametric bootstrap [8]}, [9]}, [10]}, re-estimating the weights and the marginal treatment effect for \(A\)  vs. \(C\)  in each bootstrap iteration. Point estimates, standard errors and interval estimates can be directly calculated from the bootstrap replicates.
